An interactive look at day-to-day crypto volatility

We want to further investigate these joint dynamics before returning to the analysis of cryptocurrency volatility. The results obtained at a high-frequency level for the cryptocurrency https://www.xcritical.com/ cross-section highlight an absence of the leverage effect in the traditional sense, where a negative return impacts future volatility more than a positive one. Such behavior challenges the notion of market efficiency and the traded price as an aggregator of all the available information. The hypothesis of rational expectation in front of a negative return falls in the case of cryptocurrency since it is a positive movement of the market that increases the volatility.

Is Crypto’s Volatility Bad for the Financial System?

The need for regulation crypto volatility to protect consumers to legitimise the industry has long been called for by prominent figures participating in the industry, but lawmakers have been slow to answer the calls. This pressure can be compounded further when large holders – often called whales – buy or sell significant quantities of a particular asset, potentially sending its price soaring or tumbling. The crypto markets are not yet efficient enough to absorb these supply and demand shocks without significant price impact, wholesale.

Analyze and target market trends

BitDegree aims to uncover, simplify & share Web3 & cryptocurrency education with the masses. Join millions, easily discover and understand cryptocurrencies, price charts, top crypto exchanges & wallets in Smart contract one place. In most cases, CBOE Volatility Index (VIX) is used to measure the asset’s level of volatility. However, some cryptocurrencies have their own measures for these fluctuations in price. For example, the Bitcoin Volatility Index (BitVol) or the Ethereum Volatility Index (EthVol).

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Bitcoin’s one-year realized volatility becomes particularly noteworthy when it reaches new all-time lows. These low volatility environments can become the foundation for future upward moves in price. Circled below are four instances of realized volatility hitting a new all-time low.

Selling during this frenzy and leaving before the breakout loses momentum is a great method to turn a profit safely. Volatility is spurred before and during this time and becomes worse just after the quarterly results. Traders will try to prepare, predict, and protect their crypto before the results are published.

• By design, the cryptocurrency is limited to 21 million coins—the closer the circulating supply gets to this limit, the higher prices are likely to climb.
• Blockchain is a digital ledger that’s stored on a decentralized network of computers.
• For this reason, there are now volatility indexes for some of the major cryptocurrencies.
• The graph shows the performance of two different markets over time, one with high volatility and one with low volatility.
• Katsiampa (2019) find asymmetric effects between good and bad news among cryptocurrencies.

We compute the estimators using a different number of observations to properly reflect the realized variance within a given day for both asset classes. The entities in the cryptocurrency cross-section have around four times the available observation compared to equities. They are, therefore, subject to be more volatile by construction since each entity has more recorded fluctuations. However, this difference does not impact how we construct n𝑛nitalic_n-days volatility estimators that follow the same process for both asset classes. An interesting improvement of the volatility analysis could be generated using sophisticated on-chain data, accounting for the blockchain user activity and network size measure where agents are operating. Indeed, the cryptocurrency ecosystem lives on blockchains that produce a lot of data and do not follow the same logic as the traditional financial market.

However, there are many different ways that companies and cryptocurrencies are looking to remain stable to provide a functional and viable service, as well as to aid in their adoption as a token that can be of worthwhile and valuable. Commission-free trading refers to $0 commissions charged on trades of US listed registered securities placed during the US Markets Regular Trading Hours in self-directed brokerage accounts offered by Public Investing. Keep in mind that other fees such as regulatory fees, Premium subscription fees, commissions on trades during extended trading hours, wire transfer fees, and paper statement fees may apply to your brokerage account. Public Investing is a wholly-owned subsidiary of Public Holdings, Inc. (“Public Holdings”).

In this regard, we document the asymmetric effect of returns when accounting for the intraday jump dynamics. This distinction between investor behaviors underpins the varied volatility drivers in the market. 3 shows the decomposed first lag specification estimation results when the latter effect is added. The \(\gamma\) parameter is consistently significant and with the same magnitude for all the estimation windows tested, except for the horizon equal to five days ahead. The coefficient is always negative, reflecting the inversion of the leverage effect retrieved daily. As previously studied in Baur and Dimpfl (2018); Brini and Lenz (2022), negative returns’ impact tends to lower the cryptocurrency volatility instead of increasing it, as is commonly expected from the empirical literature.

However, it is particularly nuanced in the crypto space due to the unique supply dynamics of many different digital assets. Bitcoin has only been around for 15 years– a while longer than most crypto assets, so they are still in price discovery. This means that prices will continue to fluctuate as new participants continue to enter the market trying to establish consensus on the fair value of digital assets in the process. All new financial markets take time to settle and be accepted in order to reach maturity – the same holds true for crypto. The asset class, the market, and its investors/speculators are still finding their feet during this early and high growth phase.

For instance, Chokor and Alfieri (2021) has analyzed the effect of regulation on trader activity, finding that investors reacted less negatively for most illiquid cryptocurrencies and those with higher information asymmetry. We compute the realized variance estimator described in using the 5-minute level data in for each of the cryptocurrencies. Specifically, Jha and Baur (2020); Ftiti et al. (2021) explores the analysis of cryptocurrency volatility by using intraday data of a small number of coins and tokens. Similarly, Naeem et al. (2022); Yousaf and Ali (2020) study spillover effect on the volatility using generalized vector autoregressive model versions.

Our paper ventures beyond the market standard by acknowledging that the market liquidity of Bitcoin options, even on the most liquid exchanges, is far inferior to the S&P 500 options that are the basis for the original VIX index. We therefore consider alternatives for the volatility extraction as well as index aggregation. The two resulting volatility indices are cointegrated and the corresponding error correction model can be utilized as a metric for market implied tail-risk. Similarly, Conrad et al. (2018) use a GARCH-MIDAS model to analyze long- and short-term Bitcoin volatility components and find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility.

The resulting amount is immediately cash settled in the currency of the underlying. As a first step in the empirical analysis of cryptocurrency volatility, we estimate the reference specification that accounts for three different lags of the estimated volatility observed in the past. Equation 9 are instrumental in describing past signed returns’ impact on future volatility. For both the cross-sections, the RV estimator is highly correlated to its signed components and the continuous volatility estimator BV, as expected by construction. On the contrary, the SJV estimators and their signed components are not strongly correlated with the other estimators for the equity cross-section. At the same time, such a correlation tends to be higher in the case of cryptocurrencies.

Since the invention of Bitcoin, cryptocurrencies have evolved into a new class of financial assets. Naturally, as cryptocurrency spot markets evolve, markets for derivatives thereon follow. Of those, option markets offer the unique potential to extract volatility information that would otherwise be unobservable. We extract said information through a cryptocurrency volatility index (CVX) that captures the market’s expectation of future volatility. 7 shows Pearson correlations between log-differences of major volatility indices. The correlation between Bitcoin and other volatility ranges roughly between 0.1 and 0.3, whereas classical assets show higher correlations.